QSTR Analysis of Acute Rat Oral Toxicity of Amide Pesticides

2020 ◽  
Vol 5 (2) ◽  
pp. 73-99
Author(s):  
Purusottam Banjare ◽  
Jagadish Singh ◽  
Partha Pratim Roy
Keyword(s):  
Oral Toxicity ◽  
Reliability Indicators ◽  
In Silico Studies ◽  
Oecd Principles ◽  
Structural Alerts ◽  
Prediction Reliability ◽  
Data Gap ◽  
Experimental Values ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

The rodent acute toxicity is gaining much attention in the ecotoxicological assessment of chemicals. Among the available amide pesticides, the majority of compounds are lacking the experimental toxicity values of rat oral toxicity. In order to explore the structural alerts for toxicity and to fill the toxicity data gap through in silico studies, a series of statistically robust local quantitative structure-toxicity relationship (QSTR) models were developed for the prediction of acute oral toxicity of amide pesticides on rat following OECD principles. The mechanistic interpretation indicated types of amide, the presence of halogen, and SO2 functionality were influential for the toxicity. Applicability domain (AD) analysis and prediction reliability indicators assured the robustness and reliability of the developed models. The detailed analyses of the AD as well the consensus predictions of the unknown compounds were commented for their toxic nature, and prioritization was done for similar classes of compounds without experimental values.

scholarly journals Herb-Induced Liver Injury: Phylogenetic Relationship, Structure-Toxicity Relationship, and Herb-Ingredient Network Analysis

2019 ◽  
Vol 20 (15) ◽  
pp. 3633 ◽  
Author(s):  
Shuaibing He ◽  
Chenyang Zhang ◽  
Ping Zhou ◽  
Xuelian Zhang ◽  
Tianyuan Ye ◽  
...  
Keyword(s):  
Liver Injury ◽  
Medicinal Plants ◽  
Phylogenetic Relationship ◽  
Herbal Products ◽  
Systemic Analysis ◽  
Multiple Datasets ◽  
Structural Alerts ◽  
Structure Toxicity Relationship ◽  
Relationship Structure ◽  
Toxicity Relationship

Currently, hundreds of herbal products with potential hepatotoxicity were available in the literature. A comprehensive summary and analysis focused on these potential hepatotoxic herbal products may assist in understanding herb-induced liver injury (HILI). In this work, we collected 335 hepatotoxic medicinal plants, 296 hepatotoxic ingredients, and 584 hepatoprotective ingredients through a systematic literature retrieval. Then we analyzed these data from the perspectives of phylogenetic relationship and structure-toxicity relationship. Phylogenetic analysis indicated that hepatotoxic medicinal plants tended to have a closer taxonomic relationship. By investigating the structures of the hepatotoxic ingredients, we found that alkaloids and terpenoids were the two major groups of hepatotoxicity. We also identified eight major skeletons of hepatotoxicity and reviewed their hepatotoxic mechanisms. Additionally, 15 structural alerts (SAs) for hepatotoxicity were identified based on SARpy software. These SAs will help to estimate the hepatotoxic risk of ingredients from herbs. Finally, a herb-ingredient network was constructed by integrating multiple datasets, which will assist to identify the hepatotoxic ingredients of herb/herb-formula quickly. In summary, a systemic analysis focused on HILI was conducted which will not only assist to identify the toxic molecular basis of hepatotoxic herbs but also contribute to decipher the mechanisms of HILI.

scholarly journals Study of acute oral toxicity of organotin compounds containing a 2,6-di-tert-butylphenol fragment

2021 ◽  
Vol 20 (3) ◽  
pp. 73-77
Author(s):  
M. A. Dodokhova ◽  
A. V. Safronenko ◽  
I. M. Kotieva ◽  
E. F. Komarova ◽  
V. G. Trepel ◽  
...  
Keyword(s):  
Lethal Dose ◽  
Organotin Compounds ◽  
Fixed Dose ◽  
Intragastric Administration ◽  
Oral Toxicity ◽  
Tert Butyl ◽  
Classification And Labeling ◽  
Structure Toxicity Relationship ◽  
Relationship Of ◽  
Toxicity Relationship

The aim of the study was to evaluate the safety of the use of organotin compounds containing a fragment of 2,6-di-tert-butylphenol as pharmaceutical substances when administered intragastrically to Wistar outbred rats (females). Material and methods. The objects of the study were three organotin compounds: ((3,5-di-tertbutyl-4-hydroxyphenylthiolate) triphenyltin (Me-5), (3,5-di-tert-butyl-4-hydroxyphenylthiolate)trimethyltin (Me-4), bis(3,5-di-tert-butyl-4-hydroxyphenylthiolate) dimethyltin (Me-3). Acute toxicity study were performed on 106 Wistar rats (female) weighing 190-210 g by "fixed dose" and "up and down" methods according to the OECD protocols. Results. According to the harmonized system of hazard classification and labeling of chemical products (GHS) the studied organotin compounds should be assigned to the following toxicity classes: Me-5 — IV, Me-3 — V, Me-4 — II. Average lethal dose in intragastric administration for Me-5 is LD50 = 955.0 ± 58.3 mg/kg, the value of LD50 for Me-3 is conventionally assumed to be much more than 2000 mg/kg, for Me-4 is in the range of 5 to 50 mg/kg. Discussion. The modification of tin-organic molecules in the course of directed synthesis opens broad prospects for the creation of a new class of anticancer drugs. In the course of the experimental study, the regularities of the "structure-toxicity" relationship of organic tin derivatives were revealed: the introduction of the 2,6-di-tert-butylphenol group significantly reduces toxicity compared to the corresponding initial substances; methyl derivatives are more toxic than their phenyl analogues. Compounds of GHS toxicity classes IV and V can be considered as leading candidates for promising preclinical studies in the field of experimental oncology. Conclusion. Substances of Me-3 and Me-5, which have the highest safety for intragastric use, were recommended for further study as antitumor drug agents.

Thiophenes as mosquito larvicides: Structure-toxicity relationship analysis

1991 ◽  
Vol 41 (1) ◽  
pp. 89-100 ◽  
Author(s):  
Robin J. Marles ◽  
R.Lilia Compadre ◽  
Cesar M. Compadre ◽  
Chantal Soucy-Breau ◽  
Robert W. Redmond ◽  
...  
Keyword(s):  
Relationship Analysis ◽  
Mosquito Larvicides ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

scholarly journals HUBUNGAN KUANTITATIF STRUKTUR TOKSISITAS : REVIEW

2020 ◽  
Vol 3 (2) ◽  
pp. 107-126
Author(s):  
Purwaniati Purwaniati
Keyword(s):  
In Silico ◽  
Toxicity Data ◽  
Quantitative Structure ◽  
Training Set ◽  
Drug Discovery And Development ◽  
Independent Variables ◽  
Drug Molecules ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship ◽  
In Silico Methods

AbstrakProses penemuan dan pengembangan obat merupakan proses panjang yang memerlukan banyak waktu dan biaya. Ada banyak calon molekul obat yang gagal mencapai pasaran karena alasan toksisitasnya yang tinggi, sehingga harus dapat diidentifikasi sedini mungkin. Hubungan kuantitatif struktur toksisitas (HKST) merupakan salah satu metode in silico yang cukup tangguh untuk memprediksi toksisitas. HKST merupakan persamaan matematis yang dibentuk dari variabel data endpoint toksisitas seperti LD50 sebagai variabel terikat dan sejumlah deskriptor sebagai variable bebas yang dihitung dari senyawa-senyawa dalam training set. Persamaan HKST kemudian digunakan untuk memprediksi toksisitas senyawa baru.Kata kunci : toksisitas, hubungan kuantitatif struktur toksisitas (HKST)AbstractThe process of drug discovery and development is a long process that requires a lot of time and costly. There are many prospective drug molecules that fail to reach the market due to high toxicity reasons, so they must be identified as early as possible. The quantitative structure toxicity relationship  (QSTR) is one of the in silico methods that is strong enough to predict toxicity. QSTR is a mathematical equation formed from endpoint toxicity data variables such as LD50 as a bound variable and a number of descriptors as independent variables calculated from the compounds in the training set. The QSTR equation is then used to predict the toxicity of new compounds.Keywords: toxicity, quantitative structure toxicity relationship (QSTR)

Toxicity of Halogen, Sulfur and Chlorinated Aromatic Compounds

2011 ◽  
Vol 1 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Ashutosh Kumar Gupta ◽  
Arindam Chakraborty ◽  
Santanab Giri ◽  
Venkatesan Subramanian ◽  
Pratim Chattaraj
Keyword(s):  
Aromatic Compounds ◽  
Density Functional ◽  
Inorganic Compounds ◽  
Dft Method ◽  
Qsar Model ◽  
Electrophilicity Index ◽  
Conceptual Density Functional Theory ◽  
Chlorinated Aromatic Compounds ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

In this paper, quantitative–structure–toxicity–relationship (QSTR) models are developed for predicting the toxicity of halogen, sulfur and chlorinated aromatic compounds. Two sets of compounds, containing mainly halogen and sulfur inorganic compounds in the first set and chlorinated aromatic compounds in the second, are investigated for their toxicity level with the aid of the conceptual Density Functional Theory (DFT) method. Both sets are tested with the conventional density functional descriptors and with a newly proposed net electrophilicity descriptor. Associated R2, R2CV and R2adj values reveal that in the first set, the proposed net electrophilicity descriptor (??±) provides the best result, whereas in the second set, electrophilicity index (?) and a newly proposed descriptor, net electrophilicity index (??±) provide a comparable performance. The potential of net electrophilicity index to act as descriptor in development of QSAR model is also discussed.

scholarly journals QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Medicines ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Junko Nagai ◽  
Mai Imamura ◽  
Hiroshi Sakagami ◽  
Yoshihiro Uesawa
Keyword(s):  
Prediction Model ◽  
Anticancer Drugs ◽  
Predictive Accuracy ◽  
Cell Cancer ◽  
External Validation ◽  
Selective Toxicity ◽  
Normal Cells ◽  
Validation Set ◽  
Structure Toxicity Relationship ◽  
Toxicity Relationship

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.

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